Use of an expanded gold standard to estimate the accuracy of colposcopy and visual inspection with acetic acid
Bibliographic record
Abstract
We estimate the accuracy of colposcopy and visual inspection with acetic acid (VIA) while minimizing the effects of misclassification bias, and maximizing ascertainment of disease. VIA was performed by experienced physicians on a population-based sample of women aged 30 to 49 years in rural Shanxi province, China. Each woman received VIA, liquid-based cytology (LBC) and hybrid capture 2 (hc2, QIAGEN, Gaithersburg, MD; formerly Digene Corporation). Any woman who tested positive on any test had colposcopy, endocervical curettage (ECC) with directed biopsies as necessary and 4-quadrant random biopsies from normal-appearing areas of the cervix. A standard diagnosis based on colposcopy and directed biopsy, and an expanded diagnosis including ECC and 4-quadrant random biopsy were generated for each woman. In 1,839 women, use of the expanded versus the standard diagnostic criteria increased the prevalence of histologically confirmed high-grade cervical intraepithelial neoplasia and cancer (CIN2+) from 3.2% (59/1,839) to 4.2% (77/1,839) and decreased the sensitivity of VIA for CIN2+ from 69.5% (95% CI: 56.8-79.8) to 58.4% (95% CI: 47.3-68.8%) with little change in specificity of approximately 89%. Compared with the expanded diagnostic criterion, the sensitivity of a visual diagnosis of high-grade CIN or cancer by a colposcopist was 49.4% (95% CI: 38.2-60.5). The use of an expanded diagnostic criterion in this study yielded more conservative estimates of the sensitivity of VIA and colposcopy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".